mvregmed.fit | R Documentation |
Fit regularized mediation model for a specified lambda penalty value. Structural equation models for analysis of multiple exposures (x), multiple mediators, and multiple outcome variables (y) are fit with a lasso (L1) penalaty on the model parameters. The model is x-[alpha] -> mediator-[beta] -> outcome, where alpha and beta are the parameters for the indirect effect of x on y, through the mediator. The model also allows a direct effect of x on y: x-[delta]->y.
mvregmed.fit(x, mediator, y, lambda, x.std = TRUE, med.std = TRUE, y.std = TRUE, max.outer = 5000, max.inner = 2, step.multiplier = 0.5, print.iter = FALSE, max.cor=0.99)
x |
matrix with columns representing "exposure" variable (sometimes called instrumental variable) |
mediator |
matrix with columns representing mediator variables |
y |
matrix with columns representing outcome variables |
lambda |
lambda penalty parameter |
x.std |
logical (TRUE/FALSE) whether to standardize x by dividing by standard devation of x. Note that each column of x will be centered on its mean. |
med.std |
logical (TRUE/FALSE) whether to standardize mediator by dividing by standard devation of mediator. Note that each column of mediator will be centered on its mean. |
y.std |
logical (TRUE/FALSE) whether to standardize y by dividing by standard devation of y. Note that each column of y will be centered on its mean. |
max.outer |
maximum number of outer loop iterations. The outer loop cycles over several inner loops. |
max.inner |
maximum number of iterations for each inner loop. There is an inner loop for each paramemeter in the matrices alpha, beta, delta, and vary. |
step.multiplier |
In inner loop, the step size is shrunk by the step.multiplier to assure that step size is not too large. Generally, the default of 0.5 works well. |
print.iter |
print iteration number during fitting routine |
max.cor |
maximum correlation within y, x, or mediators, so fitting is more robust |
An object of class mvregmed
Daniel Schaid and Jason Sinnwell
Schaid DJ, Dikilitas O, Sinnwell JP, Kullo I (2022). Penalized Mediation Models for Multivariate Data. Genet Epidemiol 46:32-50.
mvregmed.grid
data(medsim) mvfit <- mvregmed.fit(x, med[,1:10], y, lambda=.1) summary(mvfit)
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